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Quantum Federated Learning for Healthcare: a new paradigm for collaborative AI

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Friday, January 24, 2025
2:00 pm - 3:00 pm
Amandeep Singh Bhatia
Triangle Quantum Computing Seminar Series

The healthcare industry frequently handles sensitive and proprietary data, and due to strict privacy regulations, it is often reluctant to share it directly. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. In recent years, quantum computing, especially distributed quantum computing, including quantum machine learning (QML), has made remarkable progress. The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary approach with immense potential to revolutionize various industries, from healthcare to finance. We propose a quantum federated learning framework that significantly reduces communication rounds while achieving a higher mean receiver operating characteristic (ROC) area under the curve (AUC) on popular medical image datasets. The framework surpasses the performance of locally trained clients under unbalanced data distributions among healthcare institutions.

Contact: Margo Ginsberg